Stages: Staging, Production, Archived
Promote models through lifecycle stages safely.
Stages: Staging, Production, Archived is a free MLOps Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Stages Exist
A version number alone does not say which model is live. Stages add a label that tells everyone where each version sits in its lifecycle. 🚦
The Stage Names
MLflow gives each version one of four stages: None, Staging, Production, or Archived. A fresh version starts at None until you move it.
Staging Means Testing
Staging is your dress rehearsal. A model here is being validated and shadow-tested, but real users are not relying on it yet.
Production Is Live
Production marks the version actually serving users. Your inference code loads this stage, so promoting here changes what people really get.
Archived Means Retired
Archived keeps an old version around without serving it. You preserve history for audits while clearly signaling it should not be used.
Promote a Version
Use the client to transition a version into a stage. One call moves version 3 of your model into Staging.
c.transition_model_version_stage(
"churn-classifier", 3, "Staging")Auto-Archive the Old One
When you promote to Production, pass archive_existing_versions so the previous live version is retired in the same move, with no gap.
c.transition_model_version_stage(
"churn-classifier", 4, "Production",
archive_existing_versions=True)One Live Per Stage
Keep exactly one version in Production at a time. That way loading by stage is unambiguous and nobody guesses which model is serving.
Aliases Are the New Way
Newer MLflow favors aliases like champion over fixed stages. An alias is a movable nickname you point at any version you choose.
c.set_registered_model_alias(
"churn-classifier", "champion", 4)Promotion Is Reversible
If a promoted model misbehaves, you just transition the previous version back to Production. Rollback is a label change, not a redeploy.
Gate the Move
Treat each transition as a gate. Require passing tests or approval before a version is allowed to reach Staging or Production.
Quick Check
Pick the stage your serving code should load.
Recap
Stages turn a pile of versions into a clear lifecycle: None to Staging to Production to Archived. Promotion and rollback are now just safe label changes. ✅
Frequently asked questions
Is the “Stages: Staging, Production, Archived” lesson free?
Yes — the full text of “Stages: Staging, Production, Archived” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Stages: Staging, Production, Archived”?
Promote models through lifecycle stages safely. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Stages: Staging, Production, Archived” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Register Your First Model Version
- Stages: Staging, Production, Archived
- Add Tags and Descriptions to Models
- Load a Model Back by Name and Stage